Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 4, 2021
Date Accepted: May 6, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Predicting Kidney Graft Survival using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study
ABSTRACT
Background:
Kidney transplantation is the optimal treatment for patients with end-stage kidney disease. Short and long term kidney graft survival is influenced by a number of donor-and recipient factors. Predicting the success of kidney transplantation is important to optimize kidney allocation.
Objective:
To predict the risk of kidney graft failure across three temporal cohorts – within 1 year, 5 years, and more than 5 years after transplantation, based on donor and recipient characteristics. We analyzed a large dataset comprising over 50000 kidney transplants covering an approximate 20-year period.
Methods:
We applied machine learning based classification algorithms to develop prediction models to predict the risk of graft failure for the three different temporal cohorts. Deep learning based autoencoders were applied for data dimensionality reduction, which improved the prediction performance. Feature influence towards graft survival for each cohort was studied by investigating a new non-overlapping patient stratification approach.
Results:
Our models predicted graft survival with area under the curve (AUC) scores of 82% within 1 year, 69% within 5 years, and 81% within 17 years. The feature importance analysis elucidated the varying influence of clinical features towards graft survival across the three different temporal cohorts.
Conclusions:
We developed machine learning models to predict kidney graft survival for three temporal cohorts and analyzed the changing relevance of features over time.
Citation
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Copyright
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